Remote Sensing and Data-Driven Optimization of Water and Fertilizer Use: A Case Study of Maize Yield Estimation and Sustainable Agriculture in the Hexi Corridor

Guang Yang, Jun Wang () and Zhengyuan Qi
Additional contact information
Guang Yang: College of Information Science and Technology, Gansu Agricultural University, Lanzhou 730070, China
Jun Wang: College of Information Science and Technology, Gansu Agricultural University, Lanzhou 730070, China
Zhengyuan Qi: College of Information Science and Technology, Gansu Agricultural University, Lanzhou 730070, China

Sustainability, 2025, vol. 17, issue 18, 1-31

Abstract: Agricultural sustainability is becoming increasingly critical in the face of climate change and resource scarcity. This study presents an innovative method for maize yield estimation, integrating remote sensing data and machine learning techniques to promote sustainable agricultural development. By combining Sentinel-2 optical imagery and Sentinel-1 radar data, accurate maize classification masks were created, and the Weighted Least Squares (WLS) model achieved a coefficient of determination (R 2 ) of 0.89 and a root mean square error (RMSE) of 12.8%. Additionally, this study demonstrates the significant role of water and fertilizer optimization in enhancing agricultural sustainability, with water usage reduced by up to 14.76% in Wuwei and 10.23% in Zhangye, and nitrogen application reduced by 5.5% and 8.5%, respectively, while maintaining stable yields. This integrated approach not only increases productivity and reduces resource waste, but it also promotes environmentally friendly and efficient resource use, supporting sustainable agriculture in water-scarce regions.

Keywords: precision agriculture; crop yield prediction; sustainable agriculture; machine learning; data fusion; remote sensing technology (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/17/18/8182/pdf (application/pdf)
https://www.mdpi.com/2071-1050/17/18/8182/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:17:y:2025:i:18:p:8182-:d:1747157

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().